CatBoost–Bayesian Hybrid Model Adaptively Coupled with Modified Theoretical Equations for Estimating the Undrained Shear Strength of Clay

Abstract

The undrained shear strength of clay is an important index for the calculation of the bearing capacity of the foundation soil, the calculation of the soil pressure of the foundation pit, and the analysis of the slope stability. Therefore, the purpose of this paper is to conduct a comprehensive study of the combined use of machine learning with clay theoretical equations to estimate it. Under the Bayesian framework, the CatBoost algorithm (CatBoost–Bayesian) based on Bayesian optimization algorithm was developed to obtain the feature importance level of soil parameters affecting the undrained shear strength of clay, so as to adaptively couple the theoretical equation of undrained shear strength of K0 consolidated clay, which was derived from the modified Cambridge model. Then, the theoretical equation of undrained shear strength of the isotropically consolidated clay was established from the critical state of the clay parameters. Finally, it was illustrated and verified using the experimental samples of Finnish clay. The results indicate that the theoretical equation established by the overconsolidation ratio and effective overburden pressure parameters can well estimate the undrained shear strength of isotropically consolidated clays, and the parameter uncertainty can be considered explicitly and rigorously.